Bat Algorithm
Example:
from bejoor.swarm_based import BatAlgorithm
def func(sol):
return abs((sol[0]**sol[3] + sol[1]**sol[3] - sol[2]**sol[3]))
solution_vector = [{"type": "integer", "lower_bound": 1, "upper_bound": 500}] * 3 + \
[{"type": "integer", "lower_bound": 2, "upper_bound": 100}] + \
[{"type": "float", "lower_bound": 0, "upper_bound": 1}] * 3
# without target objective value
ba= BatAlgorithm(objective_function=func, solution_vector_size=7,
solution_vector=solution_vector, optimization_side="min",
loudness=0.3, pulse_rate=0.5, min_frequency=0.0, max_frequency=2.0,
population_size=30, epochs=50)
ba.run()
print(ba.best_solution)
print(ba.best_objective_value)
Parameters:
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objective_function: Objective function needs to be optimized.
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solution_vector_size: Vector size of the candidate solutions.
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solution_vector: A vector which determines the types of each variable in solution vectors.
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optimization_side: Determines maximize or minimize the objective function.
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target_objective_value: Target Objective value.
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target_objective_lower_bound: Target Objective lower bound.
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target_objective_upper_bound: Target Objective upper bound.
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population_size: Number of individuals in the population.
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epochs: Number of generations to run the algorithm.
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loudness: The loudness parameter, controlling local search exploitation.
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pulse_rate: The pulse rate, controlling the probability of a bat performing a local search.
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min_frequency: Minimum frequency for controlling bat velocities.
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max_frequency: Maximum frequency for controlling bat velocities.